Abstract
One problem faced in the design of Augmented Reality (AR) applications is the interference of virtually displayed objects in the user's visual field, with the current attentional focus of the user. Newly generated content can disrupt internal thought processes. If we can detect such internally-directed attention periods, the interruption could either be avoided or even used intentionally. In this work, we designed a special alignment task in AR with two conditions: one with externally-directed attention and one with internally-directed attention. Apart from the direction of attention, the two tasks were identical. During the experiment, we performed a 16-channel EEG recording, which was then used for a binary classification task. Based on selected band power features, we trained a Linear Discriminant Analysis classifier to predict the label for a 13-s window of each trial. Parameter selection, as well as the training of the classifier, were done in a person-dependent manner in a 5-fold cross-validation on the training data. We achieved an average score of approximately 85.37% accuracy on the test data (± 11.27%, range = [66.7%, 100%], 6 participants > 90%, 3 participants = 100%). Our results show that it is possible to discriminate the two states with simple machine learning mechanisms. The analysis of additionally collected data dispels doubts that we classified the difference in movement speed or task load. We conclude that a real-time assessment of internal and external attention in an AR setting in general will be possible.
Highlights
Life includes many situations where we find ourselves lost in thought or thinking about something while we were supposed to pay attention to someone or something in our outside world: a speaker, a teacher, a movie, or a colleague talking to us
We evaluated whether the classification accuracy for a participant with a classification accuracy below 80% would improve with a global feature set that is based on the features that were chosen during the classification of a participant with a high accuracy
Average and variance are computed on unrounded results (*participants with fewer trials due to technical problems, ◦ better classification accuracy than for the EEG classification)
Summary
Life includes many situations where we find ourselves lost in thought or thinking about something while we were supposed to pay attention to someone or something in our outside world: a speaker, a teacher, a movie, or a colleague talking to us. We may realize that we do not remember anything they said or did the last minutes, our attention was directed inside ourselves. “External attention refers to the selection and modulation of sensory information, as it initially comes into the mind, generally in a modality-specific representation and often with episodic tags for spatial locations and points in time. (...) Internal attention refers to the selection and modulation of internally generated information, such as the contents of working memory, long-term memory, task sets, or response selection” (Chun et al, 2011). We cannot even tell how long our mind has been fixated on something other than it was supposed to This difficulty in reporting one’s direction of attention in many situations makes it hard to judge, understand, and scientifically work with one’s attentional state. An estimation of the attentional state of a person might be helpful in several applications
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